Peripheral nerve injuries are common diseases that affect a large amount of patients every year. Tissue engineering has emerged as a powerful approach for developing alternative nerve grafts for peripheral nerve regeneration. Since tissue engineering strategies in peripheral nerve regeneration involve various possible combinations of variables, it is necessary to develop efficient tools to identify optimal tissue engineering strategies and predict the experimental results based on these tissue engineering strategies for peripheral nerve regeneration. Some research groups have applied artificial neural networks and decision trees to obtain the best model configuration for the prediction of the tissue engineering strategies. For the decision trees based methods, it is hard to tell which classification tree is better than the other. Furthermore, the prediction system using the decision tree algorithm lacks the capability of accumulating the learning experience over time. On the other hand, Artificial Neural Networks (ANNs) exhibit some remarkable properties, but only the connection weights are trained with fixed topology. It is hard to find the best fixed topology in advance for each specific tissue engineering strategy. In this proposal, swarm intelligence (SI) based evolving ANNs technique is proposed to tackle this challenge. Two swarm intelligence based methods, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), will be applied in this project to train the ANN model. More specifically, ACO will be used to optimize the topology structure of the ANN models, while the PSO is used to adjust the connection weights of the ANN models based on the optimized topology structure. For this SWarm Intelligence based Reinforcement Learning method for ANNs (SWIRL-ANN) system, both topology and connection weight of artificial neural networks can be evolved automatically and simultaneously so that an optimal classifier for tissue engineering strategies in peripheral nerve regeneration can be achieved. The research project will include the following phases: Aim 1: Predict tissue engineering strategies in peripheral nerve regeneration using SWarm Intelligence based Reinforcement Learning method for ANNs (SWIRL-ANN) analytical and prediction system. Aim 2: Validate the efficacy of novel unknown tissue engineered nerve grafts as predicted by using SWIRL-ANN based analytical and prediction system for bridging peripheral nerve gaps in rat sciatic nerve injury model in vivo. PUBLIC HEALTH RELEVANCE: Tissue engineering has emerged as a powerful approach for developing nerve grafts for peripheral nerve regeneration. Since tissue engineering strategies in peripheral nerve regeneration involve various possible combinations of variables, it is necessary to develop efficient tools to identify optimal tissue engineering strategies and predict the experimental results based on these tissue engineering strategies for peripheral nerve regeneration. In this proposal, swarm intelligence (SI) based evolving artificial neural networks (ANNs) technique is proposed to tackle this challenge. The proposed research will be helpful to efficiently develop tissue engineered products for tissue and organ replacement.